High-throughput screening for novel medical materials: machine learning-enabled approaches
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Spoorthi P. Shetty
Abstract
The adoption of machinemachine learning in biomedical research in the context of drug delivery system characteristics, drug release profiles, and the optimization of nanoparticle systems is quickly changing the face of biomedical research. This chapter seeks to apply machine learning algorithms for identification of characteristics that are time-critical for drug delivery systems including mechanical properties, degradation rate, and biocompatibility. A comparison of basic versions of regression models and deep learning is outlined to investigateinvestigate the potential for improvement of accuracy and speed when implementing drug delivery systems. Particular attention is paid to applications of the drug release kinetic models, as well as the use of ML approaches for individualized approaches to drug delivery and shorter treatment regimens, making it possible to emphasize the possible role of ontological ML strategies in increasing the efficacy of the treatment. The application of the ML in defining the appropriate parameters of designed nanoparticles and in combining the experimental and computational techniques for the fabrication of targeted and efficient delivery systems is discussed. Some of the issues that hinder the implementation of ML in drug delivery are reviewed alongside opportunities and future trends of the technology. Concerning ethical issues, and to follow safe and effective requirements for ML technologies application, further development outlines core principlesprinciples that must be followed. In this context, the provided insights for academicians, practicing clinicians, and policymakers are meant to be useful in enhancing the state of customized medicinemedicine and addressing existing and emerging chronic healthcare challenges that intend to apply ML in enhancing drug delivery systems.
Abstract
The adoption of machinemachine learning in biomedical research in the context of drug delivery system characteristics, drug release profiles, and the optimization of nanoparticle systems is quickly changing the face of biomedical research. This chapter seeks to apply machine learning algorithms for identification of characteristics that are time-critical for drug delivery systems including mechanical properties, degradation rate, and biocompatibility. A comparison of basic versions of regression models and deep learning is outlined to investigateinvestigate the potential for improvement of accuracy and speed when implementing drug delivery systems. Particular attention is paid to applications of the drug release kinetic models, as well as the use of ML approaches for individualized approaches to drug delivery and shorter treatment regimens, making it possible to emphasize the possible role of ontological ML strategies in increasing the efficacy of the treatment. The application of the ML in defining the appropriate parameters of designed nanoparticles and in combining the experimental and computational techniques for the fabrication of targeted and efficient delivery systems is discussed. Some of the issues that hinder the implementation of ML in drug delivery are reviewed alongside opportunities and future trends of the technology. Concerning ethical issues, and to follow safe and effective requirements for ML technologies application, further development outlines core principlesprinciples that must be followed. In this context, the provided insights for academicians, practicing clinicians, and policymakers are meant to be useful in enhancing the state of customized medicinemedicine and addressing existing and emerging chronic healthcare challenges that intend to apply ML in enhancing drug delivery systems.
Kapitel in diesem Buch
- Frontmatter I
- Contents V
- List of contributors VII
- Blockchain technology to secure medical data sharing in machine learning applications ensure privacy and integrity 1
- AI-powered sensors and devices for sustained health tracking 39
- Development of AI-driven biomedical sensors and devices optimization for continuous health monitoring 89
- Design and development of AI-driven biomedical sensors and devices and their optimization for continuous health monitoring 131
- Machine learning-driven personalized medicine: customized drug delivery systems and patient-specific material applications 193
- Personalized medicine using customized drug delivery systems and patient-specific material solutions, enabled by machine learning algorithms 239
- AI-driven drug design exploring molecular docking and lead optimization using machine learning algorithms 297
- Machine learning models for predicting drug toxicity and side effects 335
- Machine learning innovations in biomedical materials from drug discovery to personalized medicine 395
- High-throughput screening for novel medical materials: machine learning-enabled approaches 445
- Automated materials characterization using machine learning for screening biocompatible materials 489
- Machine learning algorithms for enhanced medical image analysis and diagnostics 541
- Transforming healthcare with machine learning 585
- Revolutionizing healthcare 635
- Index 687
- De Gruyter Series in Advanced Mechanical Engineering
Kapitel in diesem Buch
- Frontmatter I
- Contents V
- List of contributors VII
- Blockchain technology to secure medical data sharing in machine learning applications ensure privacy and integrity 1
- AI-powered sensors and devices for sustained health tracking 39
- Development of AI-driven biomedical sensors and devices optimization for continuous health monitoring 89
- Design and development of AI-driven biomedical sensors and devices and their optimization for continuous health monitoring 131
- Machine learning-driven personalized medicine: customized drug delivery systems and patient-specific material applications 193
- Personalized medicine using customized drug delivery systems and patient-specific material solutions, enabled by machine learning algorithms 239
- AI-driven drug design exploring molecular docking and lead optimization using machine learning algorithms 297
- Machine learning models for predicting drug toxicity and side effects 335
- Machine learning innovations in biomedical materials from drug discovery to personalized medicine 395
- High-throughput screening for novel medical materials: machine learning-enabled approaches 445
- Automated materials characterization using machine learning for screening biocompatible materials 489
- Machine learning algorithms for enhanced medical image analysis and diagnostics 541
- Transforming healthcare with machine learning 585
- Revolutionizing healthcare 635
- Index 687
- De Gruyter Series in Advanced Mechanical Engineering
